code: def run_river_experiment(file_path, with_tuning=False):
      """Function launch example with experimental features of the FEDOT framework
  
      :param file_path: path to the csv file
      :param with_tuning: is it need to tune pipelines or not
      """
  
      # Read dataframe and prepare train and test data
      df = pd.read_csv(file_path)
      features = np.array(
          df[["level_station_1", "mean_temp", "month", "precip"]]
      )
      target = np.array(df["level_station_2"]).reshape((-1, 1))
  
      # Prepare InputData for train and test
      train_input, predict_input, task = prepare_input_data(
          features, target
      )
      y_data_test = predict_input.target
  
      # Get refinement pipeline
      r_pipeline = get_refinement_pipeline()
      non_pipeline = get_non_refinement_pipeline()
  
      if with_tuning:
          tuner = (
              TunerBuilder(task)
              .with_tuner(SimultaneousTuner)
              .with_metric(RegressionMetricsEnum.MAE)
              .with_iterations(100)
              .build(train_input)
          )
          r_pipeline = tuner.tune(r_pipeline)
          non_pipeline = tuner.tune(non_pipeline)
  
      # Fit it
      r_pipeline.fit(train_input)
      non_pipeline.fit(train_input)
  
      # Predict
      predicted_values = r_pipeline.predict(predict_input)
      r_preds = predicted_values.predict
  
      # Predict
      predicted_values = non_pipeline.predict(predict_input)
      non_preds = predicted_values.predict
  
      y_data_test = np.ravel(y_data_test)
  
      mse_value = mean_squared_error(
          y_data_test, r_preds, squared=False
      )
      mae_value = mean_absolute_error(y_data_test, r_preds)
      print(f"RMSE with decompose - {mse_value:.2f}")
      print(f"MAE with decompose - {mae_value:.2f}\n")
  
      mse_value_non = mean_squared_error(
          y_data_test, non_preds, squared=False
      )
      mae_value_non = mean_absolute_error(y_data_test, non_preds)
      print(f"RMSE non decompose - {mse_value_non:.2f}")
      print(f"MAE non decompose - {mae_value_non:.2f}\n")
  
file_path: FEDOT/examples/advanced/decompose/regression_refinement_example.py
imports: import warnings
  import numpy
  import pandas
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from sklearn.model_selection import train_test_split
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.metrics_repository import RegressionMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 4 for calling a global function with example usage: run_river_experiment(file_path, with_tuning) without return types